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Estimating the Spartina alterniflora fractional vegetation cover using high spatial resolution remote sensing in a coastal wetland

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Spartina alterniflora is an important invasive species in coastal beach ecosystems, and its distribution and cover are basic parameters that affect the ecology of these wetlands. In this study, S.… Click to show full abstract

Spartina alterniflora is an important invasive species in coastal beach ecosystems, and its distribution and cover are basic parameters that affect the ecology of these wetlands. In this study, S. alterniflora distribution and fractional vegetation cover were investigated, using high spatial resolution satellite remote sensing images from SPOT6 (6 m), and low altitude Unmanned Aerial Vehicle (UAV) images (10 cm) from Sansha Bay ( San Du Ao). The latter is a typical coastal wetland area in Ningde, Fujian Province, China. For the extraction of SPOT6 remote sensing information, support vector machine and maximum likelihood classification methods were explored. In addition, a geographic information system (GIS) technique was used to reduce errors. As a result, an accurate S. alterniflora distribution map was obtained. Furthermore, the normalized difference vegetation index (NDVI) of image pixels was calculated for the S. alterniflora distribution area. Subsequently, with the NDVI of pure S. alterniflora pixels and pure beach soil pixels, the fractional vegetation cover could be calculated. S. alterniflora area was 20.19 km in the total study region. S. alterniflora area in the Jiaocheng district was 9.63 km and was distributed mainly in Zhangwan and Southern Jiaocheng, accounting for 47.70% of the total study area. S. alterniflora area in Fuan County was 5.20 km, was distributed mainly in Yantian Bay and Magang Bay, and accounted for 25.76%. The fractional vegetation cover of S. alterniflora was mostly a medium (40%—60%) and a

Keywords: vegetation cover; area; vegetation; remote sensing; cover; fractional vegetation

Journal Title: Acta Ecologica Sinica
Year Published: 2017

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